alexa Application of high-dimensional feature selection: evaluation for genomic prediction in man
Biomedical Sciences

Biomedical Sciences

International Journal of Biomedical Data Mining

Author(s): M L Bermingham, R PongWong, A Spiliopoulou, C Hayward, I Rudan

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In this study, we investigated the effect of five feature selection approaches on the performance of a mixed model (G-BLUP) and a Bayesian (Bayes C) prediction method. We predicted height, high density lipoprotein cholesterol (HDL) and body mass index (BMI) within 2,186 Croatian and into 810 UK individuals using genome-wide SNP data. Using all SNP information Bayes C and G-BLUP had similar predictive performance across all traits within the Croatian data, and for the highly polygenic traits height and BMI when predicting into the UK data. Bayes C outperformed G-BLUP in the prediction of HDL, which is influenced by loci of moderate size, in the UK data. Supervised feature selection of a SNP subset in the G-BLUP framework provided a flexible, generalisable and computationally efficient alternative to Bayes C; but careful evaluation of predictive performance is required when supervised feature selection has been used.

This article was published in Scientific reports and referenced in International Journal of Biomedical Data Mining

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